Computer vision-based defect detection in hot-rolled steel surfaces in industrial manufacturing.

Manufacturing Computer Vision

Most manufacturing companies train their people to manually perform a visual inspection, which is a manual process of inspection that can be subjective, resulting in an accuracy that is dependent on the experience and opinion of the individual inspector. It should also be noted that this process is labor-intensive.

In cases when there are machine calibration issues, environmental settings, or equipment malfunction, the entire batch of production may become faulty. In such cases, manual inspection after the fact may prove to be expensive, as the items may have already been produced and the entire batch of faulty products (maybe hundreds or thousands) may need to be discarded.

In summary, the manual process of inspection is slow, inaccurate, and expensive.

A computer vision-based visual inspection system can detect surface defects in real-time by analyzing streams of video frames. The system can send alerts, in real-time, when a defect or a series of defects is detected so that the production can be stopped to avoid any loss.

We have developed a machine learning-based model to detect visual defects in hot rolled steel surfaces. The system is able to detect 6 types of defects.

Here is a video of various streams of video frames detected for defects.

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